Abstract

The chronic inflammatory process is becoming usually accepted as a key determinant of dietary habits in the occurrence and development of non-communicable diseases, such as cardiovascular diseases, type 2 diabetes, and metabolic syndrome. This paper gives a biomathematical perspective of inflammation dynamics in relation to diet, combining nutritional inputs with physiological and immunological responses to gain better insight into the development of chronic diseases. The model theorizes the dietary intake as an active force that impacts pro-inflammatory and anti-inflammatory mediators, metabolic homeostasis, and tissue-level responses in the long-term. The interactions between dietary factors (including saturated fats, refined sugar and micronutrients) and inflammatory biomarkers (e.g., cytokines, C-reactive protein) and metabolic indicators (including insulin sensitivity, lipid profiles) are represented in a system of nonlinear differential equations. The model includes the element of feedback, with the continuous unhealthy dieting patterns increasing inflammatory reactions and balanced nutrition enhancing regulatory mechanisms which suppress inflammatory reactions. The empirical data of clinical and epidemiological studies inform parameterization, thus being biologically relevant and interpretable. The discussion examines a steady-state behaviour, stability conditions and threshold effects which define the transition of acute to chronic inflammation. Numerical simulations show that continuous high-calorie, low-nutrient diets cause prolonged inflammatory conditions, exposing individuals to the risk of developing chronic diseases. On the other hand, dietary interventions with high content of anti-inflammatory nutrients can demonstrate the prospect of reversing or stabilizing the inflammatory dynamics. Sensitivity analysis has determined parameters of the dietary glycemic load and fat composition as key factors that determine the behaviour of the system. The results support the significance of dietary trends as risk factors that can be modified in order to prevent and manage chronic diseases. This research fills the gap between nutrition science and systems biology by giving a quantitative framework of information on the personalized dietary strategy and the community health interventions. The suggested biomathematical model will be a useful resource in forecasting future health conditions and making evidence-based nutritional policies to mitigate the burden of chronic diseases across the globe.